• Users Online: 123
  • Print this page
  • Email this page
ORIGINAL ARTICLE
Year : 2021  |  Volume : 11  |  Issue : 4  |  Page : 237-252

Generative adversarial network image synthesis method for skin lesion generation and classification


1 Department of Computer Science and Engineering, School of Science and Engineering, Khazar University, Baku, Azerbaijan
2 Department of Computer Science and Engineering, School of Science and Engineering, Khazar University, Baku, Azerbaijan; Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
3 Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
4 Department of Electronic and Computer Engineering, Brunel University, London, UK

Correspondence Address:
Saeed Meshgini
Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jmss.JMSS_53_20

Rights and Permissions

Background: One of the common limitations in the treatment of cancer is in the early detection of this disease. The customary medical practice of cancer examination is a visual examination by the dermatologist followed by an invasive biopsy. Nonetheless, this symptomatic approach is time-consuming and prone to human errors. An automated machine learning model is essential to capacitate fast diagnoses and early treatment. Objective: The key objective of this study is to establish a fully automatic model that helps Dermatologists in skin cancer handling process in a way that could improve skin lesion classification accuracy. Method: The work is conducted following an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) using the Python-based deep learning library Keras. We incorporated effective image filtering and enhancement algorithms such as bilateral filter to enhance feature detection and extraction during training. The Deep Convolutional Generative Adversarial Network (DCGAN) needed slightly more fine-tuning to ripe a better return. Hyperparameter optimization was utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters. In this work, we decreased the learning rate from the default 0.001 to 0.0002, and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models and at each iteration the weights of the discriminative and generative network were updated to balance the loss between them. We endeavour to address a binary classification which predicts two classes present in our dataset, namely benign and malignant. More so, some well-known metrics such as the receiver operating characteristic -area under the curve and confusion matrix were incorporated for evaluating the results and classification accuracy. Results: The model generated very conceivable lesions during the early stages of the experiment and we could easily visualise a smooth transition in resolution along the way. Thus, we have achieved an overall test accuracy of 93.5% after fine-tuning most parameters of our network. Conclusion: This classification model provides spatial intelligence that could be useful in the future for cancer risk prediction. Unfortunately, it is difficult to generate high quality images that are much like the synthetic real samples and to compare different classification methods given the fact that some methods use non-public datasets for training.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed390    
    Printed4    
    Emailed0    
    PDF Downloaded63    
    Comments [Add]    

Recommend this journal